scholarly journals Condition Monitor System for Rotation Machine by CNN with Recurrence Plot

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3221 ◽  
Author(s):  
Hsueh ◽  
Ittangihala ◽  
Wu ◽  
Chang ◽  
Kuo
Keyword(s):  
Time Series ◽  
Failure Modes ◽  
Time Series Data ◽  
Induction Motors ◽  
Recognition Task ◽  
Recurrence Plot ◽  
Operating Conditions ◽  
Series Data ◽  
Recurrence Plots ◽  
Preprocessing Method

Induction motors face various stresses under operating conditions leading to some failure modes. Hence, health monitoring for motors becomes essential. In this paper, we introduce an effective framework for fault diagnosis of 3-phase induction motors. The proposed framework mainly consists of two parts. The first part explains the preprocessing method, in which the time-series data signals are converted into two-dimensional (2D) images. The preprocessing method generates recurrence plots (RP), which represent the transformation of time-series data such as 3-phase current signals into 2D texture images. The second part of the paper explains how the proposed convolutional neural network (CNN) extracts the robust features to diagnose the induction motor’s fault conditions by classifying the images. The generated RP images are considered as input for the proposed CNN in the texture image recognition task. The proposed framework is tested on the dataset collected from different 3-phase induction motors working with different failure modes. The experimental results of the proposed framework show its competitive performance over traditional methodologies and other machine learning methods.

Faithfulness of Recurrence Plots: A Mathematical Proof

2015 ◽  
Vol 25 (12) ◽  
pp. 1550168 ◽  
Author(s):  
Yoshito Hirata ◽  
Motomasa Komuro ◽  
Shunsuke Horai ◽  
Kazuyuki Aihara
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Mathematical Proof ◽  
Recurrence Plot ◽  
Series Data ◽  
Recurrence Plots ◽  
Original Time Series ◽  
Mild Conditions ◽  
Original Time ◽  
Almost All

It is practically known that a recurrence plot, a two-dimensional visualization of time series data, can contain almost all information related to the underlying dynamics except for its spatial scale because we can recover a rough shape for the original time series from the recurrence plot even if the original time series is multivariate. We here provide a mathematical proof that the metric defined by a recurrence plot [Hirata et al., 2008] is equivalent to the Euclidean metric under mild conditions.

scholarly journals Multi-Channel Fusion Classification Method Based on Time-Series Data

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4391
Author(s):  
Xue-Bo Jin ◽  
Aiqiang Yang ◽  
Tingli Su ◽  
Jian-Lei Kong ◽  
Yuting Bai
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Evidence Theory ◽  
Recurrence Plot ◽  
Series Data ◽  
Public Datasets ◽  
Application Fields ◽  
Original Time

Time-series data generally exists in many application fields, and the classification of time-series data is one of the important research directions in time-series data mining. In this paper, univariate time-series data are taken as the research object, deep learning and broad learning systems (BLSs) are the basic methods used to explore the classification of multi-modal time-series data features. Long short-term memory (LSTM), gated recurrent unit, and bidirectional LSTM networks are used to learn and test the original time-series data, and a Gramian angular field and recurrence plot are used to encode time-series data to images, and a BLS is employed for image learning and testing. Finally, to obtain the final classification results, Dempster–Shafer evidence theory (D–S evidence theory) is considered to fuse the probability outputs of the two categories. Through the testing of public datasets, the method proposed in this paper obtains competitive results, compensating for the deficiencies of using only time-series data or images for different types of datasets.

scholarly journals A Brief Introduction to Nonlinear Time Series Analysis and Recurrence Plots

Vibration ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 332-368 ◽  
Author(s):  
Bedartha Goswami
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Nonlinear Time Series ◽  
Recurrence Plot ◽  
Series Data ◽  
Nonlinear Time Series Analysis ◽  
Recurrence Plots ◽  
Series Analysis ◽  
Wide Range ◽  
Dynamical Features

Nonlinear time series analysis gained prominence from the late 1980s on, primarily because of its ability to characterize, analyze, and predict nontrivial features in data sets that stem from a wide range of fields such as finance, music, human physiology, cognitive science, astrophysics, climate, and engineering. More recently, recurrence plots, initially proposed as a visual tool for the analysis of complex systems, have proven to be a powerful framework to quantify and reveal nontrivial dynamical features in time series data. This tutorial review provides a brief introduction to the fundamentals of nonlinear time series analysis, before discussing in greater detail a few (out of the many existing) approaches of recurrence plot-based analysis of time series. In particular, it focusses on recurrence plot-based measures which characterize dynamical features such as determinism, synchronization, and regime changes. The concept of surrogate-based hypothesis testing, which is crucial to drawing any inference from data analyses, is also discussed. Finally, the presented recurrence plot approaches are applied to two climatic indices related to the equatorial and North Pacific regions, and their dynamical behavior and their interrelations are investigated.

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